{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. \n",
    "One idea along these lines is batch normalization which was proposed by [1] in 2015.\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [1] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n",
    "\n",
    "def print_mean_std(x,axis=0):\n",
    "    print('  means: ', x.mean(axis=axis))\n",
    "    print('  stds:  ', x.std(axis=axis))\n",
    "    print() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n",
    "\n",
    "Referencing the paper linked to above in [1] may be helpful!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:   [27.18502186 34.21455511 37.68611762]\n",
      "\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  means:  [7.10542736e-17 7.82707232e-17 9.71445147e-18]\n",
      "  stds:   [0.99999999 1.         1.        ]\n",
      "\n",
      "After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n",
      "  means:  [11. 12. 13.]\n",
      "  stds:   [0.99999999 1.99999999 2.99999999]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print_mean_std(a,axis=0)\n",
    "\n",
    "gamma = np.ones((D3,))\n",
    "beta = np.zeros((D3,))\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)\n",
    "\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:   [1.01531428 1.01238373 0.97819988]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.7029241291468676e-09\n",
      "dgamma error:  7.420414216247087e-13\n",
      "dbeta error:  2.8795057655839487e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "#You should expect to see relative errors between 1e-13 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too!  \n",
    "\n",
    "In the forward pass, given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n",
    "\n",
    "we first calculate the mean $\\mu$ and variance $v$.\n",
    "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma$  and normalized data $Y$.\n",
    "The equations and graph illustration below describe the computation ($y_i$ is the i-th element of the vector $Y$).\n",
    "\n",
    "\\begin{align}\n",
    "& \\mu=\\frac{1}{N}\\sum_{k=1}^N x_k  &  v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2 \\\\\n",
    "& \\sigma=\\sqrt{v+\\epsilon}         &  y_i=\\frac{x_i-\\mu}{\\sigma}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"notebook_images/batchnorm_graph.png\" width=691 height=202>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "The meat of our problem during backpropagation is to compute $\\frac{\\partial L}{\\partial X}$, given the upstream gradient we receive, $\\frac{\\partial L}{\\partial Y}.$ To do this, recall the chain rule in calculus gives us $\\frac{\\partial L}{\\partial X} = \\frac{\\partial L}{\\partial Y} \\cdot \\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "The unknown/hart part is $\\frac{\\partial Y}{\\partial X}$. We can find this by first deriving step-by-step our local gradients at \n",
    "$\\frac{\\partial v}{\\partial X}$, $\\frac{\\partial \\mu}{\\partial X}$,\n",
    "$\\frac{\\partial \\sigma}{\\partial v}$, \n",
    "$\\frac{\\partial Y}{\\partial \\sigma}$, and $\\frac{\\partial Y}{\\partial \\mu}$,\n",
    "and then use the chain rule to compose these gradients (which appear in the form of vectors!) appropriately to compute $\\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "If it's challenging to directly reason about the gradients over $X$ and $Y$ which require matrix multiplication, try reasoning about the gradients in terms of individual elements $x_i$ and $y_i$ first: in that case, you will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. \n",
    "\n",
    "You should make sure each of the intermediary gradient derivations are all as simplified as possible, for ease of implementation. \n",
    "\n",
    "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  9.20004371222927e-13\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 0.24x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.2611955101340957\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 5.65e-06\n",
      "W3 relative error: 4.14e-10\n",
      "b1 relative error: 4.44e-08\n",
      "b2 relative error: 4.44e-08\n",
      "b3 relative error: 1.02e-10\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.17e-09\n",
      "gamma1 relative error: 7.47e-09\n",
      "gamma2 relative error: 3.35e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.996533220108303\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.29e-06\n",
      "W3 relative error: 2.79e-08\n",
      "b1 relative error: 5.55e-09\n",
      "b2 relative error: 4.44e-08\n",
      "b3 relative error: 2.10e-10\n",
      "beta1 relative error: 6.32e-09\n",
      "beta2 relative error: 3.48e-09\n",
      "gamma1 relative error: 6.27e-09\n",
      "gamma2 relative error: 4.14e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "# You should expect losses between 1e-4~1e-10 for W, \n",
    "# losses between 1e-08~1e-10 for b,\n",
    "# and losses between 1e-08~1e-09 for beta and gammas.\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            normalization='batchnorm')\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver with batch norm:\n",
      "(Iteration 1 / 200) loss: 2.340975\n",
      "(Epoch 0 / 10) train acc: 0.107000; val_acc: 0.115000\n",
      "(Epoch 1 / 10) train acc: 0.314000; val_acc: 0.266000\n",
      "(Iteration 21 / 200) loss: 2.039365\n",
      "(Epoch 2 / 10) train acc: 0.390000; val_acc: 0.279000\n",
      "(Iteration 41 / 200) loss: 2.036710\n",
      "(Epoch 3 / 10) train acc: 0.497000; val_acc: 0.315000\n",
      "(Iteration 61 / 200) loss: 1.769536\n",
      "(Epoch 4 / 10) train acc: 0.531000; val_acc: 0.321000\n",
      "(Iteration 81 / 200) loss: 1.265761\n",
      "(Epoch 5 / 10) train acc: 0.593000; val_acc: 0.319000\n",
      "(Iteration 101 / 200) loss: 1.261353\n",
      "(Epoch 6 / 10) train acc: 0.657000; val_acc: 0.320000\n",
      "(Iteration 121 / 200) loss: 1.101662\n",
      "(Epoch 7 / 10) train acc: 0.698000; val_acc: 0.319000\n",
      "(Iteration 141 / 200) loss: 1.212343\n",
      "(Epoch 8 / 10) train acc: 0.696000; val_acc: 0.310000\n",
      "(Iteration 161 / 200) loss: 0.783686\n",
      "(Epoch 9 / 10) train acc: 0.776000; val_acc: 0.324000\n",
      "(Iteration 181 / 200) loss: 0.890417\n",
      "(Epoch 10 / 10) train acc: 0.759000; val_acc: 0.328000\n",
      "\n",
      "Solver without batch norm:\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.129000; val_acc: 0.131000\n",
      "(Epoch 1 / 10) train acc: 0.283000; val_acc: 0.250000\n",
      "(Iteration 21 / 200) loss: 2.041970\n",
      "(Epoch 2 / 10) train acc: 0.316000; val_acc: 0.277000\n",
      "(Iteration 41 / 200) loss: 1.900473\n",
      "(Epoch 3 / 10) train acc: 0.373000; val_acc: 0.282000\n",
      "(Iteration 61 / 200) loss: 1.713156\n",
      "(Epoch 4 / 10) train acc: 0.390000; val_acc: 0.310000\n",
      "(Iteration 81 / 200) loss: 1.662209\n",
      "(Epoch 5 / 10) train acc: 0.434000; val_acc: 0.300000\n",
      "(Iteration 101 / 200) loss: 1.696059\n",
      "(Epoch 6 / 10) train acc: 0.535000; val_acc: 0.345000\n",
      "(Iteration 121 / 200) loss: 1.557986\n",
      "(Epoch 7 / 10) train acc: 0.530000; val_acc: 0.304000\n",
      "(Iteration 141 / 200) loss: 1.432189\n",
      "(Epoch 8 / 10) train acc: 0.628000; val_acc: 0.339000\n",
      "(Iteration 161 / 200) loss: 1.033932\n",
      "(Epoch 9 / 10) train acc: 0.661000; val_acc: 0.340000\n",
      "(Iteration 181 / 200) loss: 0.901035\n",
      "(Epoch 10 / 10) train acc: 0.726000; val_acc: 0.318000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "print('Solver with batch norm:')\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True,print_every=20)\n",
    "bn_solver.train()\n",
    "\n",
    "print('\\nSolver without batch norm:')\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n",
    "    \"\"\"utility function for plotting training history\"\"\"\n",
    "    plt.title(title)\n",
    "    plt.xlabel(label)\n",
    "    bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n",
    "    bl_plot = plot_fn(baseline)\n",
    "    num_bn = len(bn_plots)\n",
    "    for i in range(num_bn):\n",
    "        label='with_norm'\n",
    "        if labels is not None:\n",
    "            label += str(labels[i])\n",
    "        plt.plot(bn_plots[i], bn_marker, label=label)\n",
    "    label='baseline'\n",
    "    if labels is not None:\n",
    "        label += str(labels[0])\n",
    "    plt.plot(bl_plot, bl_marker, label=label)\n",
    "    plt.legend(loc='lower center', ncol=num_bn+1) \n",
    "\n",
    "    \n",
    "plt.subplot(3, 1, 1)\n",
    "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n",
    "                      lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n",
    "plt.subplot(3, 1, 2)\n",
    "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "plt.subplot(3, 1, 3)\n",
    "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lsm/文档/CS231n/Assignment/assignment2/cs231n/classifiers/fc_net.py:423: RuntimeWarning: divide by zero encountered in log\n",
      "  correct_logprobs = -np.log(probs[range(N),y])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers_ws = {}\n",
    "solvers_ws = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "  print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "  bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "  model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "  bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  bn_solver.train()\n",
    "  bn_solvers_ws[weight_scale] = bn_solver\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  solver.train()\n",
    "  solvers_ws[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 1:\n",
    "Describe the results of this experiment. How does the scale of weight initialization affect models with/without batch normalization differently, and why?\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n",
    "总体上，weight_scale从0.0001到0.1的时候，无论是否进行了batch normalization都表现的越来越好（在训练集，验证集，总损失上），但超过了0.1的时候，就会变差；总体上有batch normalization表现好一点，对参数的初始化也不那么敏感"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and batch size\n",
    "We will now run a small experiment to study the interaction of batch normalization and batch size.\n",
    "\n",
    "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    }
   ],
   "source": [
    "def run_batchsize_experiments(normalization_mode):\n",
    "    np.random.seed(231)\n",
    "    # Try training a very deep net with batchnorm\n",
    "    hidden_dims = [100, 100, 100, 100, 100]\n",
    "    num_train = 1000\n",
    "    small_data = {\n",
    "      'X_train': data['X_train'][:num_train],\n",
    "      'y_train': data['y_train'][:num_train],\n",
    "      'X_val': data['X_val'],\n",
    "      'y_val': data['y_val'],\n",
    "    }\n",
    "    n_epochs=10\n",
    "    weight_scale = 2e-2\n",
    "    batch_sizes = [5,10,50]\n",
    "    lr = 10**(-3.5)\n",
    "    solver_bsize = batch_sizes[0]\n",
    "\n",
    "    print('No normalization: batch size = ',solver_bsize)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "    solver = Solver(model, small_data,\n",
    "                    num_epochs=n_epochs, batch_size=solver_bsize,\n",
    "                    update_rule='adam',\n",
    "                    optim_config={\n",
    "                      'learning_rate': lr,\n",
    "                    },\n",
    "                    verbose=False)\n",
    "    solver.train()\n",
    "    \n",
    "    bn_solvers = []\n",
    "    for i in range(len(batch_sizes)):\n",
    "        b_size=batch_sizes[i]\n",
    "        print('Normalization: batch size = ',b_size)\n",
    "        bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n",
    "        bn_solver = Solver(bn_model, small_data,\n",
    "                        num_epochs=n_epochs, batch_size=b_size,\n",
    "                        update_rule='adam',\n",
    "                        optim_config={\n",
    "                          'learning_rate': lr,\n",
    "                        },\n",
    "                        verbose=False)\n",
    "        bn_solver.train()\n",
    "        bn_solvers.append(bn_solver)\n",
    "        \n",
    "    return bn_solvers, solver, batch_sizes\n",
    "\n",
    "batch_sizes = [5,10,50]\n",
    "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 2:\n",
    "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n",
    "似乎batch normalization在batch size大的时候表现得好一点，batch size只有5的时候就比没有batch normalization的时候还差，这因为进行bn操作的时候，还是不能太小的batch size，因为太小以后，算出来的均值和方差不能代表一整个训练集的分布，所以不好，之后用的时候一定不能把这个查超参数设得太小"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization\n",
    "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n",
    "\n",
    "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [2]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n",
    "\n",
    "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 3:\n",
    "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n",
    "\n",
    "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n",
    "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1.  \n",
    "3. Subtracting the mean image of the dataset from each image in the dataset.\n",
    "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization: Implementation\n",
    "\n",
    "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n",
    "\n",
    "Here's what you need to do:\n",
    "\n",
    "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the cell below to check your results.\n",
    "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the second cell below to check your results.\n",
    "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n",
    "\n",
    "Run the third cell below to run the batch size experiment on layer normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before layer normalization:\n",
      "  means:  [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n",
      "  stds:   [10.07429373 28.39478981 35.28360729  4.01831507]\n",
      "\n",
      "After layer normalization (gamma=1, beta=0)\n",
      "  means:  [ 4.81096644e-16 -7.40148683e-17  2.22044605e-16 -5.92118946e-16]\n",
      "  stds:   [0.99999995 0.99999999 1.         0.99999969]\n",
      "\n",
      "After layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n",
      "  means:  [5. 5. 5. 5.]\n",
      "  stds:   [2.99999985 2.99999998 2.99999999 2.99999907]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after layer normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 =4, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before layer normalization:')\n",
    "print_mean_std(a,axis=1)\n",
    "\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After layer normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)\n",
    "\n",
    "gamma = np.asarray([3.0,3.0,3.0])\n",
    "beta = np.asarray([5.0,5.0,5.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.433615657860454e-09\n",
      "dgamma error:  4.519489546032799e-12\n",
      "dbeta error:  2.276445013433725e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "ln_param = {}\n",
    "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n",
    "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n",
    "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n",
    "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n",
    "\n",
    "#You should expect to see relative errors between 1e-12 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization and batch size\n",
    "\n",
    "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 4:\n",
    "When is layer normalization likely to not work well, and why?\n",
    "\n",
    "1. Using it in a very deep network\n",
    "2. Having a very small dimension of features\n",
    "3. Having a high regularization term\n",
    "\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
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 },
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 "nbformat_minor": 2
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